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13 June 2024 Blur-CAM: visual interpretation of deep convolutional networks based on Gaussian fuzzy
Haoxiang Liu
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Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131806R (2024) https://doi.org/10.1117/12.3034083
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
As deep learning continues to advance, the complexity of its network structures has also increased. The application of visualization techniques to elucidate the learning mechanisms of deep convolutional neural networks constitutes a crucial aspect of research in interpretable artificial intelligence and the field of deep learning. Numerous visualization methods have emerged to interpret these black-box architectures, enabling analysis and comprehension of internal decision-making processes. In this paper, we introduce Blur-CAM, a method that employs Gaussian blur to process both masked and unmasked regions, enhancing object understanding and providing smoother optimization transitions for object localization in images. We evaluate our approach on the ILSVRC 2012 and PASCAL VOC 2012 datasets, achieving commendable results.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Haoxiang Liu "Blur-CAM: visual interpretation of deep convolutional networks based on Gaussian fuzzy", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131806R (13 June 2024); https://doi.org/10.1117/12.3034083
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KEYWORDS
Visualization
Deep learning
Visual process modeling
Convolutional neural networks
Object detection
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